1. Field of the Invention
Embodiments described herein pertain to the field of computer software. More particularly, but not by way of limitation, one or more embodiments enable systems to apply a set of rules to determine if two or more data objects are similar in accordance with the defined set of rules
2. Description of the Related Art
Modern businesses have a need to utilize stored business data to make effective business decisions. When the data in these systems is not shared and made consistent, inefficiencies occur. Achieving consistent data across multiple distributed heterogeneous systems is difficult. Establishing effective communication links between disparate systems is a prerequisite to making the data consistent, but does not alone solve the problem. Even when data is effectively shared throughout an organization, problems still arise in that over the course of time the data may exist in different forms and models. Since it is common for companies to maintain data in independent realms the achievement of data consistency is difficult. For example, because of the difficulties associated with merging data, some companies independently maintain data for each of their different corporate divisions and only utilize such data for business decisions relevant to a particular corporate division. However, the maintenance of data from independent systems is often desirable during mergers and acquisitions where company systems are almost certainly heterogeneous and typically utilize radically different structures and data models.
To solve the data consistency problem and leverage the commonalities of data for the benefit of the company, companies typically seek to coordinate interaction between heterogeneous systems by identifying similar and overlapping data and then coordinating the integration of such data in a way that ensures the data stays consistent across different systems. Effectively accomplishing such coordination is difficult at best and tends to lead to organizational inefficiencies. One approach some organizations use is to maintain what is known as master data. Master data may be thought of as the definitive version of a data object. Solutions for coordinating the data, i.e., storing, augmenting and consolidating master data, are generally primitive and lack matching capabilities. Moreover, the fact that master data may exist does little to provide information technology personnel with insight about the process used in determining if an object matches another object.
Master data management systems simplify maintenance and promote data integrity by simplifying the user's view of the data stored in its repository. For example, SAP's Master Data Management Environment (MDME) system is an integrated system for master data management that uses a Structured Query Language (SQL) database management system (DBMS), but does not require designers to use SQL for searching, sorting, and retrieving of information. Standard SQL DBMS do not support the types of advanced structures necessary for managing master databases. Generally speaking, master data management systems consist of a thick shell of functionality on top of a SQL-based DBMS to provide a scalable database where data is fully accessible to other applications and tools.
Failing to successfully coordinate master data objects when merging heterogeneous data bases may yield data object redundancies and inconsistencies that compromise the data, disrupt the business decision-making process and increase the overall cost of doing business. Furthermore, customer service may suffer from incomplete data requiring customers to call multiple places within the same company to obtain the required information. In some cases the failure to efficiently service customers may cause enough frustration that it begins to result in decreased customer loyalty and leads to a loss of customers.
Because of the limitations described above there is a need for a system and method that can effectively coordinate master data management data objects across an enterprise.